Falling Under the Lens of Suspicion: Identifying Behavioral Factors that Generate Suspicion

陷入怀疑的镜头下：识别产生怀疑的行为因素

• 批准号: 2146834
• 负责人: Jessica Salerno
• 金额: $ 37.97万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146834&HistoricalAwards=false
• 关键词: Falling; Under; Lens; Suspicion; Identifying

In every false conviction case, there came a point in the investigation when an innocent person became a suspect. Despite the countless accounts of how this presumed guilt can start a chain reaction of confirmation bias that taints all stages of the criminal justice process, little is known about what triggers suspicion about the innocent person in the first place. Drawing from existing and to-be-created databases, the overarching goal of this project is to identify and understand what demographic, behavioral, and linguistic factors initially spark suspicion and trigger dangerous confirmation bias processes leading to false convictions. We focus on one of the earliest moments in which a person might become a suspect: when they call 911. A key hypothesis is that when witnesses’ behaviors violate expectations, people “morally typecast” the witness as less capable of being a victim and more capable of being a perpetrator. Increasing basic understanding on how these expectations are formed, how they are violated, and how they interact with witnesses’ gender and race, is a critical step towards predicting whether callers are ultimately charged with the crime they are reporting. Moreover, understanding how these processes emerge will be critical in developing state-of-the-art curriculum to educate police about their expectations’ accuracy and consequences, and to educate attorneys on defending clients they believe to be innocent. Given the serious consequences that false convictions have on both innocent individuals and the public trust in the criminal justice system at large, the project’s focus has broad societal impact by systematically investigating the source of detectives’ misguided “hunches” that have anecdotally led to false convictions. This study employs a variety of data-driven methods to identify predictors of suspicion and being charged with the crime one is reporting. First, we will create a large corpus of real 911 calls to analyze linguistic and acoustic behavioral aspects of reporting a violent crime to see if laypeople and law enforcements’ expectations are accurate. We will recruit lay people, police officers, 911 operators, and trauma clinicians to listen to these calls and report their impressions of the caller to identify what aspects of reporting a violent crime generates suspicion and predicts actually becoming a suspect in the crime. We will identify psychological explanations for these effects as well as factors that might moderate these effects, such the caller’s race and gender. Across studies, we will use indirect, data-driven ways of assessing what makes people suspicious without imposing the researchers’ hypotheses on the design, such as using natural language processing machine-learning models. We will also test downstream consequences of witnesses’ emotion expression in a 911 call for the likelihood of being falsely convicted at trial for the crime they reported. Finally, we will quantitatively code cases of known innocence (exoneree case files) for what sparked suspicion about the innocent person, focusing on mentions of their behavior seeming “unusual”.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在每一个虚假定罪的案件中，在调查过程中都会有一个无辜的人成为嫌疑人。尽管有无数关于这种假定有罪如何引发确认偏见的连锁反应，污染刑事司法程序的所有阶段，但人们对最初是什么引发对无辜者的怀疑知之甚少。从现有的和将要创建的数据库中，该项目的总体目标是识别和了解哪些人口统计，行为和语言因素最初引发怀疑，并触发危险的确认偏见过程，导致错误的信念。我们关注的是一个人可能成为嫌疑人的最早时刻之一：当他们拨打911时。一个关键的假设是，当证人的行为违反预期时，人们“道德地定型”证人作为受害者的能力较低，而更有能力成为犯罪者。提高对这些期望如何形成、如何被违反以及它们如何与证人的性别和种族相互作用的基本理解，是预测打电话者最终是否被指控犯有他们所报告的罪行的关键一步。此外，了解这些过程是如何出现的将是至关重要的，在开发国家的最先进的课程，教育警察对他们的期望的准确性和后果，并教育律师辩护客户，他们认为是无辜的。鉴于错误定罪对无辜的个人和公众对刑事司法系统的信任造成的严重后果，该项目的重点是系统地调查侦探误导的“预感”的来源，从而产生广泛的社会影响。 这项研究采用了各种数据驱动的方法来确定怀疑的预测因素，并被指控犯有所报告的罪行。首先，我们将创建一个真实的911电话的大型语料库，以分析报告暴力犯罪的语言和声学行为方面，看看外行和执法人员的期望是否准确。我们将招募非专业人士、警察、911接线员和创伤临床医生来倾听这些电话，并报告他们对来电者的印象，以确定报告暴力犯罪的哪些方面会产生怀疑，并预测实际上会成为犯罪嫌疑人。我们将确定这些影响的心理学解释以及可能缓和这些影响的因素，如呼叫者的种族和性别。在整个研究中，我们将使用间接的、数据驱动的方式来评估是什么让人们产生怀疑，而不会将研究人员的假设强加给设计，比如使用自然语言处理机器学习模型。我们还将测试证人在911报警电话中情绪表达的下游后果，以确定他们在审判中被错误定罪的可能性。最后，我们将量化编码已知无辜的案件（exoneree案件档案），以了解是什么引发了对无辜者的怀疑，重点是他们的行为似乎“不寻常”的提及。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。